Springer 2009
Journal of Business Ethics
DOI 10.1007/s10551-009-0275-x
Why Do College Students Cheat?
ABSTRACT. More is known about the pervasiveness of
college cheating than reasons why students cheat. This
article reports the results of a study that applied the theory
of reasoned action and partial least squares methodology
to analyze the responses of 144 students to a survey on
cheating behavior. Approximately 60% of the business
students and 64% of the non-business students admitted to
such behavior. Among cheaters, a ‘‘desire to get ahead’’
was the most important motivating factor – a surprising
result given the comprehensive set of factors tested in the
study. Among non-cheaters, the presence of a ‘‘moral
anchor’’ such as an ethical professor was most important.
The article also includes a set of important caveats that
might limit this study and suggests some avenues for
further study.
KEY WORDS: cheating, ethical behavior, student dishonesty, student misconduct, theory of reasoned action
Introduction
On April 27, 2007, the Dean of the Fuqua College
of Business at Duke University announced that 24
students – nearly 10% of the graduating class of 2008
– had been caught cheating on a final exam (Conlin,
2007). A year later, the school was still dealing
with the fallout from the incident, which included
expelling the guilty students, readmitting and
counseling the suspended ones, and dealing with the
national attention garnered by the event (Damast,
2008).
A large body of research suggests that the student
cheating uncovered at Duke is not an isolated event,
but rather a microcosm of a pervasive and growing
part of worldwide university activity. However,
while a large number of individuals and organizations express concern for such trends, less is known
about what to do about it or, more importantly, how
to reverse it.
Mark G. Simkin
Alexander McLeod
The purpose of our research was to study this
problem in greater depth. In particular, we wanted
to test the hypothesis that the theory of reasoned
action (TRA) can explain cheating behavior, detect
its most important causal influences, and identify
what factors motivate students to cheat. We also
wanted to know what factors are most likely to deter
students from cheating – a very real and important
objective to teaching faculty.
The next section of this article discusses student
cheating in greater depth, identifies the major
stakeholders in the problem, and explains why
cheating is important to them. In turn, the third
section of the article discusses the TRA, presents our
hypotheses, and describes the partial-least-squares
methodology we used to test them. The fourth
section presents our results, the fifth section presents
some caveats and directions for further research, and
the last section summarizes our discussions and
presents our conclusions.
Literature review
A variety of interested parties and stakeholders agree
that cheating at the college level has become problematic. Who are these interested parties and why
their concern?
The importance of college cheating
Perhaps of greatest import is the fact that cheating in
college classes is now best described as ‘‘rampant.’’ A
meta study by Whitley (1998), for example, found
that across 46 studies, an average of 70.4% of the
college students have cheated in college. In newer
studies (Klien et al., 2007; McCabe et al., 2006;
Rokovski and Levy, 2007), the means were 70%,
Mark G. Simkin and Alexander McLeod
86%, and 60%, respectively. Viewed in an historical
perspective, there is also considerable evidence that
college cheating is growing (Rokovski and Levy,
2007). A study by Bowers (1964), for example,
found that only 26% of students admitted to some
form of copying in college, compared to 52% in a
similar study conducted in 1994 (McCabe and
Bowers, 1994). Similarly, Ogilby (1995)
found that self-reported student cheating in colleges
increased from 23% to 84% in the years from 1940 to
1982.
Recent experiences with such financial disasters as
Enron, Worldcom, and Tyco Corporations have led
the general public to ask ‘‘how can such things
happen?’’ (Gulli et al., 2007). Thus, a third reason
why college cheating may be important is because of
the suspected link between such behavior in academia and subsequent unethical behavior in the
workplace (Thompson, 2000). A number of studies
have found a strong relationship between ‘‘cheating’’
at college and ‘‘unethical behavior’’ at work. Sims
(1993), for example, found a high correlation between these two factors, leading him to conclude
that dishonesty was less a matter of ‘‘an immediate
opportunity to cheat’’ and more dependent upon ‘‘a
general attitude about honesty in the workplace.’’
Similarly, Nonis and Smith (2001) found that the
tendency to cheat at work was highly correlated
with the frequency of cheating in college – a finding
echoed by Davis and Ludvigson (1995), Swift et al.
(1998), and Crown and Spiller (1998). Finally,
Lawson (2004) found a similar relationship between
‘‘unethical workplace behavior’’ and ‘‘college
cheating.’’
Those who develop and administer certification
examinations are particularly concerned stakeholders
in the matter of cheating. Examples include the
American Institute of Certified Public Accountants
(which develops the CPA examination), the (ISC)2
(which administers the Certified Information Systems Security Professional examination), and software vendors such as Microsoft (who conduct a
variety of information technology certification
examinations). A study conducted by the Association of Test Publishers in 2007, for example,
revealed that 75% of them found evidence of
cheating on their certification examinations, and
most developers also reported that copies of past, and
sometimes current, examinations were available for
sale on the Internet (Lavelle, 2008; Thibodeau,
2007). In a chilling recreation of a common form of
college cheating, surrogates are also available for hire
to take certification examinations in return for fees
up to several thousand dollars (Thibodeau, 2007).
More recently, a number of authors have noted
that technology has given students greater access to
learning resources on the Internet, but has also
increased the number of ways that students can cheat
(Etter et al., 2006). The Internet provides a channel
for purchasing term papers, course test banks, and
solution manuals to class textbooks from Internet
vendors. Emailing friends the answers to examination or homework questions to be given or covered
in later sections of classes, is a new twist on information sharing. A real-time example would be using
text messaging to send test answers during examinations, or employing cell phones to take pictures and
email test materials to others.
Finally, a number of writers have begun to
question the concept of what constitutes ‘‘academic
dishonesty’’ and, therefore, what are punishable
offenses. If success in the corporate world requires
teamwork, they argue, then ‘‘shared information’’
and ‘‘group success’’ should be the tools by which to
measure academic performance, not individual
efforts (Conlin, 2007). For example, Robert I. Sutton, Dean of the Stanford University School of
Design, recently stated ‘‘If you found somebody to
help you write an exam, in our view that’s a sign of
an inventive person who gets stuff done’’ (Conlin,
2007). Few academicians known to these authors
share Dean Sutton’s view. Most of our colleagues
feel that widespread cheating at a university tarnishes
the reputation of the institution, demeans the value
of the degrees granted at them, and disappoints those
employers who find that student graduates cannot
adequately perform the work suggested by their
majors (Knowledge, 2004).
Cheating and colleges of business
Business schools would appear to have a particularly
strong interest in cheating activity. We have already
identified one reason for this – the apparent link
between ‘‘cheating in college’’ and ‘‘cheating in the
workplace.’’ Studies consistently find that the propensity to cheat in college carries over to the
Why Do College Students Cheat?
workplace – a concern of particular interest for
professional schools preparing students for business
careers. The hope is that ethical behavior, if
understood and internalized at the college level, will
carry over to future employment.
A related matter is the growing public expectation
that business programs include components that
teach ethical behavior. In the field of insurance, for
example, Eastman et al. (2008) note that ethical
behavior impacts property-liability and life insurance
business as well as the reputations, business success,
and professional relationships of those working in
the field. This is one reason why the Association for
the Advancement of Collegiate Schools of Business
(AACSB) accreditation requirements include the
mandate to teach business ethics as a formal and
required component of an applicant school’s
undergraduate degree programs (AACSB, 2009).
A third reason why colleges of business should be
concerned with student cheating is the growing
body of empirical evidence that, despite the widespread inclusion of course segments about ethical
behavior, business students continue to cheat more
than non-business students. For example, a study by
Harris (1989) found that business majors have lower
ethics than other majors. Similarly, Eastman et al.
(1996) found that insurance students have significantly lower levels of ethics than insurance professionals, and Carauna and Ewing (2000) found that
business students had the highest cheating rate
among business, engineering, science, and humanities students.
A final reason why colleges of business are concerned with student cheating is the belief that such
behavior tarnishes the reputation and perceived
quality of those educational institutions that experience blatant episodes of cheating, or that appear to
tolerate it (Gulli et al., 2007). This concern is
especially important to private institutions which
must necessarily compete with public schools for
both student enrollments and alumni donations.
Explaining college cheating with the theory
of reasoned action
The widespread practice of college cheating is perhaps better understood than the reasons why college
students cheat. After all, ‘‘cheating’’ would appear to
be an overt act and one that requires some effort on
the part of the participants. Why do college students
cheat?
Cheating motivators
One possible explanatory factor may simply be
‘‘opportunity.’’ Although such happenstance might
not apply in proctored-examination environments,
this explanation seems more appropriate in situations
where students have access to online resources. In a
study of plagiarism, for example, Abdolmohammadi
and Baker (2008) found that the papers from over
one-third of their undergraduate students and over
20% of their graduate students were copied from
web sources.
A second possible explanation is the ‘‘desire to
succeed.’’ If ‘‘winning is everything,’’ then cheating
simply becomes a tool to use in pursuit of this higher
goal. Such an attitude is surprising to the authors
because it seems to conflict with the goals of ‘‘group
success’’ that now pervades much of K-12 education. Limited time constraints – e.g., because of
athletic activities – or the perception that cheating is
a natural part of a student’s culture – may reinforce
this thinking.
A third possible explanation why college students
cheat is the small or non-existent penalties that some
instructors impose for infractions. A growing number of universities known to these authors, for
example, now insist that faculty at most assign a
grade of ‘‘zero’’ for the assignment or test on which
students cheated – and this only if an instructor both
catches, and is able to prove, that a student cheated.
Yet, a fourth possible explanation for college
cheating is the reluctance many professors now
harbor to prosecute student cheaters – a trend that
again enhances the environment for such behavior.
At the authors’ school, for example, instructors must
document student misconduct, and, if challenged by
the accused student(s), prove their claim in open
hearings. The belief that the penalized and resentful
students who remain in classes after such incidents
‘‘poison’’ the class environment and negatively affect
subsequent student evaluations of the class and the
professor adds to this reluctance – thereby leading to
a more forgiving, and perhaps permissive, environment for such behavior.
Mark G. Simkin and Alexander McLeod
A fifth explanation for college cheating is a
growing trend to redefine what constitutes ‘‘cheating.’’ McCabe et al. (2006), founder and president of
Duke University’s Center for Academic Integrity,
states that ‘‘stealing a glance on a test, a bit of plagiarism [is] just not on people’s radar screen anymore’’ (McCabe et al., 2006).
A final factor that might explain cheating behavior – or more accurately, explain why some students
do not cheat – is ‘‘moral code.’’ In their study, for
example, Abdolmohammadi and Baker (2008) found
that ‘‘moral reasoning’’ was a significant variable in a
linear regression of such explanatory factors, and,
therefore, seemed to explain why students with high
moral codes engaged in less cheating than those
without them.
Methodology
The theory of reasoned action framework
Although modeling something as variable as
human behavior is fraught with difficulties, several
researchers have attempted to create abstract representations of student integrity. Relevant studies
include those involving economic students (Bisping
et al., 2008), engineering students (Harding et al.,
2007; Yeo, 2007), marketing majors (Chapman
et al., 2004), marketing and management students
(Kisamore et al., 2007), business majors (Wilson,
2008), and criminal justice and legal studies students
(Lanier, 2006).
The fundamental question the authors wanted to
address is ‘‘why do college students cheat?’’ We
began with a fundamental tenant, widely cited in the
literature, that cheating is not a random, accidental,
or impulsive act, but rather a premeditated, intentional, deliberate one that requires forethought and
planning (Deci and Ryan, 2000). Given this premise, the TRA developed by Ajzen and Fishbein
(1980) would appear to be an excellent tool for
evaluating the intention to cheat.
At its core, TRA asserts that an individual’s beliefs, value system, and referential figures (e.g., parents, teachers, or peers) explain subsequent planned
behavior. TRA is widely recognized today as a
practical framework for explaining rational human
behavior, and has proven a valuable aid in explaining
a wide variety of diverse behavioral phenomena
Motivation
Availability
Gaming
Getting Ahead
Time Demands
Attitude Toward
Cheating
Culture
Moral
Risk
Intention to Cheat
Deterrent
Family
Friends
Subjective Norm
Professors
Referent
Figure 1. Theory of reasoned action framework.
(Sheppard et al., 1988), including criminal recidivism (Kiriakidis, 2008), Internet purchasing activities
(Barkhi, 2008), and athlete training patterns
(Anderson and Lavallee, 2008). We therefore considered it to be a useful tool for the exploratory task
we sought to accomplish here.
Figure 1 provides the specific TRA construct we
used for our study. Thus, our model includes what the
literature identifies as major determinants of cheating,
including ‘‘availability,’’ ‘‘gaming,’’ ‘‘getting ahead,’’
‘‘time demands,’’ ‘‘culture,’’ ‘‘morals,’’ and ‘‘risk,’’ as
reflective indicators. Items related to the influence of
‘‘family,’’ ‘‘friends,’’ and ‘‘professors’’ were relatively
independent, causing, forming, or changing the student’s subjective norm and were, therefore, categorized them as ‘‘formative variables’’ in our model.
Since both attitude and subjective norm have been
shown to affect intentions in numerous previous
studies, we also included the effect of such referents to
the individual student’s subjective norm.
Procedure
In order to measure the effects of the factors and
referents discussed above upon student cheating
behavior, the authors developed a survey which they
administered at a major public university in the
western United States. The survey respondents were
the students taking a required MIS class in this
school’s college of business. Although participation
in the study was voluntary, the promise of extra
homework credit resulted in the majority of the
students in all six sections of the course anonymously
Why Do College Students Cheat?
completing the online web survey. We note that our
use of an online survey instrument allowed students
to answer anonymously and at a time and setting of
their convenience. The MIS class itself, however,
was taught as a traditional (non-online) course.
This study had three major objectives. First, we
wanted to test TRA as a useful model of cheating
behavior. Second, if our model was viable, we
wanted to measure the relative strength of the factors
identified above as causal predictors of cheating
activity. Finally, we were interested in examining
the differences between self-reported cheaters and
non-cheaters. In other words, we wanted to know
whether the causal factors motivating these two
groups were the same.
We note that the answers to these questions extend beyond the normative ability to model a particular type of human behavior. Our ultimate goal
was to determine how best to deter student cheating
and encourage ethical conduct – an objective that
requires a deeper understanding of cheating and
non-cheating behavior. If, for example, students
cheat simply because they feel that others are
cheating, the corrective action for this is much different from the one if students cheat because they
have little fear of detection.
Sample
A total of 158 students completed our survey. Best
practices using partial least squares (PLS) analysis
discussed below require researchers to deal with
missing data in respondent surveys. Possible treatments are (1) replace missing values with mean
values, (2) replace missing values with a regressed
value, or (3) eliminate the associated survey response
from further consideration. We chose to remove
observations with missing values. Our final sample,
therefore, contained 144 usable responses.
In our final sample, 66 respondents were female
and 78 were male. The mean age of a participant was
22.5 years with a standard deviation of 4.02 years.
Probably because the participants were taking a ‘‘300level class,’’ the average student had a ‘‘junior’’ class
standing. The self-reported mean number of class
hours was 13.9 with a standard deviation of 3.4.
Thirty-nine students reported working while
attending classes. The average time a student worked
was 24.4 hours per week with a standard deviation of
10.69. Of the 144 respondents, 87 (60%) reported that
they had cheated on an average of 6.1 times. A total of
57 students stated they had never cheated.
Partial least squares analysis
We used PLS to analyze the data based on structural
equation modeling techniques (Chin et al., 2003;
Gefen and Straub, 2005). There were several reasons
for this choice. PLS makes fewer demands on the
underlying data distribution and sample size, and it is
also capable of analyzing both reflective and formative indicators (Chin, 1998b). Because of these
advantages, PLS analysis is now commonly used in
conducting behavioral systems research and provides
a robust way of analyzing survey data (Chin, 1998a;
Chin et al., 2003; Gefen and Straub, 2005; Gefen
et al., 2000).
This study used SmartPLS (Ringle et al., 2005) to
model our reflective indicators (i.e., behavioral beliefs) and our formative indicators (i.e., the independent referent items). In order to analyze the
psychometric properties of the reflective measures, we
calculated the Average Variance Extracted (AVE),
Composite Reliability (qc), Cronbach’s Alpha (CA),
Latent Variable Correlations and Cross Loadings.
Table I reports the AVE, qc, and CA for the latent variables. Although there is no standard method
for calculating statistically acceptable composites, the
generally accepted rule is for composite reliability to
be greater than 0.7 (Yi and Davis, 2003). In this
study, the lowest composite reliability was for Risk
at 0.83, thereby demonstrating sufficient reliability
for all the constructs.
The latent variable correlations and factor loadings were derived in accordance with Gefen and
Straub (2005) using SmartPLS and are provided in
the Appendix. Reliabilities of individual items were
examined by verifying loadings greater than 0.7.
One loading (C4) was marginal at 0.67. However,
all cross loadings for this variable were much less
than this loading. Eleven of the 22 indicators loaded
greater than 0.9, 10 indicators loaded greater than
0.8 and only the one mentioned here, C4, loaded at
less than 0.7. Overall, therefore, we felt that these
results demonstrated good discriminant and convergent validity.
Mark G. Simkin and Alexander McLeod
to cheat (R2 = 0.58). Student attitude toward cheating accounted for a considerable amount of this
variance (R2 = 0.62).
TABLE I
AVE, qc, and Cronbach’s Alpha
Formative indicators
AVE
qc
CA
Attitude toward cheating
Availability
Culture
Getting ahead
Intention to cheat
Morals
Risk
Time demands
0.80
0.79
0.72
0.80
0.88
0.79
0.71
0.75
0.92
0.92
0.88
0.92
0.96
0.88
0.83
0.90
0.87
0.86
0.80
0.87
0.93
0.70
0.69
0.84
Global fit measure of TRA model
Tenenhause et al. (2005) suggest a global goodnessof-fit measure for PLS modeling—GoF (0 <
GoF < 1). This fit measure is determined by taking
the square root of the product of the geometric mean
of the average communality and the average
R2 of endogenous constructs - GoF = sqrt(average(AVE)*average(Rsq)). Wetzels et al. (2009) propose a cut-off value for communality of 0.5, as
suggested by Fornell and Larcker (1981). The purpose
of this modification to GoF was to establish R2 effect
size based on Cohen (1988). By substituting 0.50 for
the minimum average AVE, GoF criteria for small,
medium and large effect sizes were set at the following values: GoFsmall = 0.1, GoFmedium = 0.25, and
GoFlarge = 0.36. These values serve as baseline values
for validating PLS models globally. Calculating this
value for our model produced a GoF = 0.55 which
Analysis and results
We formulated our structural path model to test the
TRA framework. We calculated the PLS path values
and followed with a bootstrap re-sampling method,
generating 500 samples to evaluate our model. We
then calculated the statistical significance for each path
using t-tests. Figure 2 shows the b coefficients and p
values extracted via PLS. The model accounted for a
substantial portion of variance in individual intention
Motivation
A1
A2
(ß) pvalue
***= p<.01
**= p<.05
*=p<.10
Availability
A3
GA1
C1
Culture
M1
M2
R1
ATC2
ATC3
00
***
) 0 .6 4
Attitude Toward
Cheating R 2 =.62
ITC1
.2 7
)0
1 1 4 **
(0 .
0
0.
7)
.0
(- 0
(0
.5
9)
0.
00
**
(0
Risk
.2
0
4)
20
0.
Friends
.0
)
23
(0
.3
4)
0
.
(0
(0.20) 0.25
9*
Subjective Norm
Family
ITC3
Intention to Cheat
R 2 =.58
Moral
R2
ITC2
*
(-0
C3
(0 .0 8
ATC1
0.
.3
9
Deterrent
5)
.0
4)
0
Time Demands
.4
9
.9
)0
01
(0
T3
C2
.0
T1
T2
Getting Ahead
(-0
GA2
GA3
Professors
Referent
Figure 2. All respondents b, p value, R2.
.0
1
**
*
Why Do College Students Cheat?
exceeds the GoFlarge = 0.36 suggesting that our TRA
model performs well when compared to these baseline values (Wetzels et al., 2009).
All student findings
Table II reports our statistical findings for all students
involved in the survey. We found that both ‘‘student
attitude toward cheating’’ and ‘‘subjective norm’’
were significant determinants of cheating within the
TRA model. In other words, both objective influences and subjective norms appear to affect a student’s decision to cheat. The coefficient for ‘‘attitude
toward cheating’’ was b = 0.59 with p < 0.01, and
the coefficient for ‘‘subjective norm’’ was b = 0.24
with p < 0.01.
It is only logical that not all those factors that
might affect student cheating do so equally. In this
study, we found only one statistically significant
motivator: a student’s ‘‘desire to get ahead’’
(b = 0.45 and p < 0.01). Neither ‘‘opportunity to
cheat’’ (‘‘availability’’) nor ‘‘time demands’’ seemed
to strongly influence student cheating behavior. To
us, this is consistent with TRA, which suggests that
cheating is better explained by underlying motivational forces (in this case, the ‘‘desire for advancement’’) than by opportunistic or environmental
ones. In short, these results suggest that ‘‘cheating’’ is
a reasoned, deliberate action rather than an accidental or spontaneous one.
It is also interesting to ask ‘‘what deters a student
from cheating?’’ In this study, we identified only one
statistically significant deterrent: ‘‘moral beliefs.’’
These were inversely related to cheating with b =
-0.07 and p < 0.05. In addition, we also found one
referent marginally influenced this group’s subjective
norm – ‘‘family,’’ with b = 0.34 and p = 0.09.
Of equal interest to us were the two deterrents
that did not appear to affect cheating behavior:
‘‘culture’’ and ‘‘risk.’’ This suggests that neither
culture (i.e., the ‘‘acceptability’’ of cheating as a
cultural norm) nor the risks involved (and attendant
fear of penalties) dissuades students from cheating.
The absence of ‘‘risk’’ is particularly interesting to us
because it implies that our students do not worry
much about getting caught cheating. This finding
makes us wonder whether (1) the risks of detection
at our school are abnormally low (e.g., because of
large classes or lax vigilance) or (2) the penalties for
getting caught cheating in our classes are too mild.
Since the referent variables were modeled
reflectively, they were not reported in the PLS
analysis. Table III details the path value, t-test and p
value for the referent variables. Interestingly for all
students, ‘‘friends’’ (i.e., the influence of peers) and
‘‘professors’’ (the influence of professors either as
TABLE III
Referent b, t-test, p value
Family
Friends
Professors
b
t-test
p value
0.34
0.20
0.23
1.69
1.15
1.28
0.09*
0.25
0.20
***p < 0.01; **p < 0.05; *p < 0.10.
TABLE II
All respondent results – mean, standard deviation, t-test and p-value
Original sample Sample mean Standard deviation t-test
Attitude toward cheating ﬁ intention to cheat
Availability ﬁ attitude toward cheating
Culture ﬁ attitude toward cheating
Getting ahead ﬁ attitude toward cheating
Morals ﬁ attitude toward cheating
Risk ﬁ attitude toward cheating
Subjective norm ﬁ to cheat
Time demands ﬁ attitude toward cheating
***p < 0.01; **p < 0.05; *p < 0.10.
0.59
0.00
0.14
0.51
-0.16
-0.07
0.24
0.08
0.56
0.00
0.14
0.52
-0.16
-0.08
0.28
0.07
0.10
0.06
0.10
0.11
0.06
0.06
0.09
0.12
7.27
0.02
1.46
5.03
2.63
1.19
3.57
0.64
p-value
0.00***
0.99
0.15
0.00***
0.01***
0.24
0.00***
0.53
Mark G. Simkin and Alexander McLeod
moral anchors or as enforcers) did not appear to
impact a student’s intention to cheat.
relationship with ‘‘attitude toward cheating,’’ but
neither ‘‘morals’’ nor ‘‘risk’’ was significant for noncheaters. ‘‘Time demands’’ did not significantly
influence attitude toward cheating for either group,
suggesting that ‘‘an insufficient amount of time’’ for
class work or studying – the type of problems that
might be voiced by student athletes or students who
were working in outside jobs – was not a strong
influence on cheating behavior in this sample. This
was an interesting finding to us because, campuswide, a large proportion of our students work full or
part time.
Table V reports p values for both cheater’s and
non-cheater’s subjective norm formative indicators,
and shows that the referent variables for ‘‘cheaters’’
also varied from the ‘‘non-cheater’’ sample. For
example, while the opinions of ‘‘family’’ remained a
significant influence on cheaters’ subjective norms,
Cheaters versus non-cheater findings
It is possible that the motivations and deterrents for
cheaters are different than those for non-cheaters.
Treating our sample as a homogeneous group has
the potential to hide such differences. For this reason, we split our data set into two groups – cheaters
(n = 87) and non-cheaters (n = 57) – and analyzed
each set independently. We used the same structural
model for both groups and in agreement with
Wetzels et al. (2009) believe that the number of
respondents for each group is adequate for independent analysis.
Table IV reports our results and reveals some
interesting differences. Similar to the general group,
TRA constructs for cheaters versus non-cheaters
were highly significant. In particular, ‘‘attitude toward cheating’’ was statistically significant, as was
‘‘subjective norm,’’ suggesting that the responses for
both cheaters and non-cheaters support TRA.
Interestingly, there was a marked difference between
the groups concerning ‘‘getting ahead.’’ For cheaters, ‘‘getting ahead’’ was a significant determinant of
attitude toward cheating, while for non-cheaters, it
was not.
Two other constructs were important for cheaters. ‘‘Morals’’ and ‘‘risk’’ had a significant inverse
TABLE V
Cheaters versus non-cheaters, referent b, t-test, p value
Cheaters
b
Family
0.15
Friends
0.36
Professors 0.17
Non-cheaters
t-test p value
1.98
1.19
0.65
0.05**
0.24
0.52
b
t-test p value
0.05
0.25
0.34
0.87
0.93
2.30
0.39
0.36
0.02**
***p < 0.01; **p < 0.05; *p < 0.10.
TABLE IV
Cheaters versus non-cheaters b, t-test, p value
Cheaters (n = 87)
Attitude toward cheating ﬁ intention to cheat
Availability ﬁ attitude toward cheating
Culture ﬁ attitude toward cheating
Getting ahead ﬁ toward cheating
Morals ﬁ attitude toward cheating
Risk ﬁ attitude toward cheating
Subjective norm ﬁ to cheat
Time demands ﬁ attitude toward cheating
***p < 0.01; **p < 0.05; *p < 0.10.
Non-cheaters (n = 57)
b
t-test
p value
b
t-test
p value
0.56
0.01
0.14
0.63
-0.19
-0.15
0.30
-0.05
6.22
0.02
1.54
5.44
2.82
2.12
2.45
0.28
0.00***
0.98
0.13
0.00***
0.01***
0.04**
0.02**
0.78
0.42
0.09
0.17
0.24
-0.17
-0.01
0.48
0.23
3.06
0.84
0.70
1.00
1.23
0.02
3.18
0.63
0.00***
0.40
0.49
0.32
0.22
0.98
0.00***
0.53
Why Do College Students Cheat?
neither ‘‘friends’’ nor ‘‘professors’’ appeared to have
a strong impact on ‘‘non-cheaters.’’ What we think
this means is that parents, siblings, and other family
members are likely to influence a cheater’s intention
to cheat, in contrast to non-cheater choices, where
‘‘family’’ influences were not strong. We wonder
whether students are learning that cheating is
acceptable at home.
For non-cheaters, the influence of ‘‘professors’’
was significant, suggesting that the opinions of
‘‘family’’ and ‘‘friends’’ are less likely to be an
important factor in a student’s decision to cheat. In a
sense, this is good news. It suggests to us that professors may sometimes act as ‘‘moral anchors’’ and
positively influence students not to cheat.
Caveats and directions for further research
The subject of ‘‘cheating’’ is a delicate matter, as
are the results of the studies that attempt to investigate the determinants of such behavior. One
obvious concern to us was the use of voluntary and
self-reported data concerning behavioral intention
(Sheppard et al., 1988). This is particularly problematic for the task at hand, inasmuch as we asked
our respondents to self-report infractions of their
own university’s student code of conduct. The facts
that (1) students answered anonymously, and (2) our
final percentage of 60% is consistent with other
studies mitigate, but probably do not completely
overcome, these concerns.
We also note that we used a consistent and
invariant construct to model human behavior, which
is often neither. In our defense, we note that several
researchers have attempted to model academic
integrity including engineering students (Harding
et al., 2007; Yeo, 2007), marketing majors (Chapman et al., 2004), marketing and management students (Kisamore et al., 2007), business majors
(Wilson, 2008), and criminal justice and legal studies
students (Lanier, 2006). However, we also note that
the statistical reliability that we found for our model
may be an anomaly, and we believe that further
testing is appropriate.
A third caveat pertains to the venue within which
we conducted our study, which was limited to the
students in various sections of one class at one university. Although our results are consistent with
earlier findings on the widespread prevalence of
cheating, these findings must be confined to the
context within which they were made – a single,
multi-section MIS class required of all business majors (and some non-business majors) at a major,
western university. One obvious direction for further study is to perform similar analyses at alternate
schools such as at private institutions and/or in
classes taken, say, by all university students (such as
English, Mathematics, or Western Civilizations
classes). Similarly, it may also be useful to distinguish
between ‘‘student cheating in required courses’’
versus ‘‘student cheating in elective ones.’’
A fourth caveat is the fact that, both in the interests
of brevity and expediency, we did not examine every
conceivable reason that might motivate a student
to cheat, or constrain a student from cheating, on a
test or assignment. Instead, we focused on what we
identified as the major determinants of cheating, but
recognize that we might have missed the one
important factor that causes a particular student to
cheat on a particular examination or plagiarize on a
particular term paper. Similarly, our model did not
allow us to examine the cross-product effects of our
determinants – for example, to identify what factors
deter a student with the desire to get ahead from
cheating. Again, these seem as important avenues for
further study.
A fifth concern for us is the possibility that the
factors that motivate cheaters or restrain noncheaters may differ by the type of cheating involved.
We recognize the possibility that those factors that
lead to cheating, say, on a take-home assignment or
test may not be the same ones that lead to cheating
on an in-class examination. A larger sample, with
more questions that distinguish between these different types of activities, is required to address this
issue.
Finally, we note that our survey was taken during
particularly challenging economic times. The extent
to which such circumstances encourage cheating
behavior is unknown to us, but we recognize again
that such factors as ‘‘the need to get ahead at all cost’’
might be stronger during such periods than in
alternate, more prosperous circumstances.
The results of our model suggest several additional
directions for future research. One particularly
interesting one to us is to investigate further
how ‘‘professors’’ influence student cheating. For
Mark G. Simkin and Alexander McLeod
example, does the ‘‘role of moral anchor’’ mean that
professors must exhibit exemplary behavior in the
classroom or should they simply enforce rules that
deter cheating? One promising way to answer such
questions may be to use the theory of planned
behavior (TPB) instead of the TRA as a model of
student cheating. This TPB framework may be a
superior choice because it includes a measure of
perceived behavioral control and, as Miller (2005)
points out, ‘‘…involves the addition of one major
predictor, perceived behavioral control, to the
model. In particular, this addition accounts for times
when people have the intention of carrying out a
behavior, but are thwarted because they lack confidence or complete control over such behavior’’
(Miller, p. 127). Attitudinal differences between
cheaters and non-cheaters and how the moral anchor
role may be implemented would be important to
both academia and practitioners. TPB may be able to
isolate these differing attitudes.
Conclusions
Student cheating in college appears to be both a
pervasive and growing phenomenon. This trend is
of particular concern to colleges of business, who
not only commonly teach business ethics but often
find that cheating is even more common among
business students than among non-business students.
The fundamental question of interest to the authors was not ‘‘do college students cheat’’ (which,
unfortunately, appears to be well-answered in the
affirmative), but ‘‘why do students cheat?’’ In order
to answer this question, we used the TRA and PLS
methodology to analyze 158 voluntary student surveys that asked questions on cheating behavior. Our
major findings were as follows: (1) Approximately
60% of business students admitted to having cheated
at least once while attending college. (2) The most
important reason why the students in our sample
cheated was the ‘‘desire to get ahead.’’ (3) A surprising result was that this factor appears to be more
important than such alternate, but seemingly equally
relevant, variables as ‘‘attitude towards cheating,’’
‘‘opportunity to cheat, ‘‘cultural or moral acceptance
of cheating as an established norm,’’ ‘‘low risk of
detection,’’ or ‘‘heavy time demands.’’
By separating the cheaters from the non-cheaters,
we also found one important reason why students
refrain from cheating: the presence of a moral anchor
in a faculty member whose opinion mattered. This
finding adds to the literature on cheating and offers
hope to academic faculty that their efforts to restrain
students from cheating are both needed and valuable.
We realize that our results our tentative, and
should be treated with care. Among our caveats are:
(1) the imprecision of modeling any type of human
behavior, (2) the limitations of our survey including
the setting (a single course), the type of student
(those taking a particular college of business class),
and our choice of causal variables and referents, (3)
the confounding that might have resulted from
treating all types of cheating behavior the same, and
(4) the deteriorating economic environment within
which we conducted our survey.
Finally, our results also suggest some potential
prescriptive action for college faculty and administrators. For example, because cheater’s perceptions of
‘‘getting ahead’’ appear to significantly affect their
attitude toward cheating, studying cases involving
individuals who cheat to get ahead but who subsequently suffer negative consequences might be useful.
Another avenue could be reinforcement of an intolerant collegiate culture about cheating – i.e., build a
moral culture that encourages students to ‘‘do what is
right’’ rather than ‘‘do what seems personally best.’’
Again, cases that emphasize this particular point might
be effective as well as have the potential to reinforce
the thinking of non-cheaters. Finally, because ‘‘low
risk’’ seems to affect cheating behavior, professors
might want to take class time to clearly define
cheating and unequivocally identify the potential
negative outcomes of cheating behavior.
Acknowledgments
The authors wish to thank the editors and four anonymous reviewers for their helpful comments and suggestions in revising earlier drafts of this manuscript.
Why Do College Students Cheat?
Appendix
See Tables VI and VII.
TABLE VI
Latent variable correlations
Attitude toward Availability Culture Getting Intention Morals
cheating
ahead to cheat
Attitude toward cheating
Availability
Culture
Getting ahead
Intention to cheat
Morals
Risk
Time demands
1.00
0.26
0.59
0.75
0.73
-0.50
-0.30
0.68
1.00
0.19
0.37
0.30
-0.08
-0.09
0.32
1.00
0.62
0.57
-0.39
-0.28
0.66
1.00
0.59
-0.45
-0.22
0.81
1.00
-0.49
-0.28
0.58
Risk
Time demands
1.00
0.31
1.00
-0.44 -0.35
1.00
TABLE VII
Latent variable loadings
ATC1
ATC2
ATC3
A1
A3
A4
C1
C2
C4
GA1
GA2
GA4
ITC1
ITC2
ITC3
M1
M3
R3
R4
T2
T3
T4
Attitude toward
cheating
Availability
Culture
Getting
ahead
Intention
to cheat
Morals
Risk
Time
demands
0.85
0.93
0.90
0.24
0.22
0.23
0.58
0.57
0.31
0.76
0.66
0.56
0.75
0.63
0.67
-0.42
-0.47
-0.27
-0.24
0.70
0.48
0.53
0.25
0.21
0.24
0.89
0.91
0.86
0.18
0.24
0.00
0.36
0.33
0.29
0.27
0.26
0.30
-0.03
-0.11
-0.15
0.02
0.32
0.27
0.23
0.47
0.58
0.54
0.13
0.21
0.17
0.91
0.94
0.67
0.64
0.53
0.49
0.54
0.54
0.53
-0.32
-0.37
-0.26
-0.20
0.55
0.56
0.59
0.63
0.69
0.69
0.30
0.35
0.33
0.64
0.57
0.31
0.91
0.92
0.84
0.59
0.53
0.56
-0.41
-0.40
-0.21
-0.17
0.80
0.58
0.68
0.62
0.66
0.68
0.24
0.30
0.26
0.55
0.53
0.35
0.61
0.54
0.42
0.95
0.94
0.93
-0.37
-0.49
-0.24
-0.23
0.62
0.44
0.39
-0.46
-0.42
-0.47
-0.03
-0.11
-0.08
-0.41
-0.33
-0.23
-0.46
-0.42
-0.31
-0.43
-0.51
-0.43
0.88
0.90
0.29
0.23
-0.41
-0.41
-0.31
-0.26
-0.28
-0.28
-0.12
-0.04
-0.07
-0.30
-0.24
-0.13
-0.21
-0.13
-0.27
-0.29
-0.27
-0.24
0.21
0.33
0.87
0.82
-0.28
-0.32
-0.32
0.60
0.65
0.57
0.27
0.30
0.29
0.72
0.58
0.27
0.71
0.71
0.78
0.59
0.51
0.51
-0.38
-0.40
-0.33
-0.26
0.86
0.85
0.89
Mark G. Simkin and Alexander McLeod
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College of Business Administration,
University of Nevada,
Reno, NV, U.S.A.
E-mail: [email protected]